#ifndef CAFFE_SIGMOID_CROSS_ENTROPY_LOSS_LAYER_HPP_
#define CAFFE_SIGMOID_CROSS_ENTROPY_LOSS_LAYER_HPP_

#include <vector>
#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/layers/loss_layer.hpp"
#include "caffe/layers/sigmoid_layer.hpp"


namespace caffe {

/* @brief Computes the cross-entropy (logistic) loss @f$
 *        E = \frac{-1}{n} \sum\limits_{n=1}^N \left[p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right]
 *        @f$, often used for predicting targets interpreted as probabilities.
 * This layer is implemented rather than separate SigmoidLayer + CrossEntropyLayer
 * as its gradient computation is more numerically stable.
 * At test time, this layer can be replaced simply by a SigmoidLayer.
 *
 * @param bottom input Blob vector (length 2)
 *   -# @f$ (N \times C \times H \times W) @f$ the scores @f$ x \in [-\infty, +\infty]@f$,
 *      which this layer maps to probability predictions @f$ \hat{p}_n = \sigma(x_n) \in [0, 1] @f$
 *      using the sigmoid function @f$ \sigma(.) @f$ (see SigmoidLayer).
 *   -# @f$ (N \times C \times H \times W) @f$ the targets @f$ y \in [0, 1] @f$
 * @param top output Blob vector (length 1)
 *   -# @f$ (1 \times 1 \times 1 \times 1) @f$ the computed cross-entropy loss: @f$
 *      E = \frac{-1}{n} \sum\limits_{n=1}^N \left[p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] @f$ */
template <typename Dtype>
class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
 public:
  explicit SigmoidCrossEntropyLossLayer(const LayerParameter& param) : LossLayer<Dtype>(param), sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
                                                                       sigmoid_output_(new Blob<Dtype>()) {}
  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
  virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
  virtual inline const char* type() const { return "SigmoidCrossEntropyLoss"; }

 protected:
  /// @copydoc SigmoidCrossEntropyLossLayer
  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);

  /* @brief Computes the sigmoid cross-entropy loss error gradient w.r.t. the predictions.
   * Gradients cannot be computed with respect to the target inputs (bottom[1]),
   * so this method ignores bottom[1] and requires !propagate_down[1], crashing if propagate_down[1] is set.
   *
   * @param top output Blob vector (length 1), providing the error gradient with respect to the outputs
   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
   *      as @f$ \lambda @f$ is the coefficient of this layer's output
   *      @f$\ell_i@f$ in the overall Net loss
   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
   *      (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
   * @param propagate_down see Layer::Backward.
   *      propagate_down[1] must be false as gradient computation with respect to the targets is not implemented.
   * @param bottom input Blob vector (length 2)
   *   -# @f$ (N \times C \times H \times W) @f$ the predictions @f$x@f$; Backward computes diff
   *      @f$ \frac{\partial E}{\partial x} = \frac{1}{n} \sum\limits_{n=1}^N (\hat{p}_n - p_n) @f$
   *   -# @f$ (N \times 1 \times 1 \times 1) @f$ the labels -- ignored as we can't compute their error gradients */
  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);

  /// Read the normalization mode parameter and compute the normalizer based on the blob size. 
  /// If normalization_mode is VALID, the count of valid outputs will be read from valid_count,
  /// unless it is -1 in which case all outputs are assumed to be valid.
  virtual Dtype get_normalizer(LossParameter_NormalizationMode normalization_mode, int valid_count);

  /// The internal SigmoidLayer used to map predictions to probabilities.
  shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
  /// sigmoid_output stores the output of the SigmoidLayer.
  shared_ptr<Blob<Dtype> > sigmoid_output_;
  /// bottom vector holder to call the underlying SigmoidLayer::Forward
  vector<Blob<Dtype>*> sigmoid_bottom_vec_;
  /// top vector holder to call the underlying SigmoidLayer::Forward
  vector<Blob<Dtype>*> sigmoid_top_vec_;

  /// Whether to ignore instances with a certain label.
  bool has_ignore_label_;
  /// The label indicating that an instance should be ignored.
  int ignore_label_;
  /// How to normalize the loss.
  LossParameter_NormalizationMode normalization_;
  Dtype normalizer_;
  int outer_num_, inner_num_;
};

}  // namespace caffe
#endif  // CAFFE_SIGMOID_CROSS_ENTROPY_LOSS_LAYER_HPP_
